Katzilla MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies the data source (U.S. Department of the Treasury), update frequency (daily), return format (Katzilla envelope with data, quality, citation), and audit features (SHA-256 hash). No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first covers purpose, scope, and usage; the second details source, updates, and return format. Every sentence adds critical information without redundancy, making it front-loaded and zero-waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, 100% schema coverage, annotations, and an output schema implied by the return format description), the description is complete. It covers purpose, usage, behavioral traits, source details, and output structure, compensating well for any gaps. With annotations and schema handling technical aspects, no additional explanation is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 5 parameters (dataset, startDate, endDate, limit, sort). The description adds minimal parameter semantics beyond the schema, only mentioning dataset selection and date-range filtering generically, which aligns with the schema but doesn't provide extra details like dataset examples or format specifics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the action ('Access'), resource ('100+ Treasury fiscal datasets'), and scope ('interest rates, revenue, spending, securities, and more'), making the purpose highly specific. It distinguishes itself from sibling tools like 'economic__treasury-debt' by focusing on broader fiscal data rather than just debt, and from other economic tools by specifying the Treasury source.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: for accessing Treasury fiscal datasets with date-range filtering. It implies usage for economic/financial data needs but does not explicitly state when not to use it or name specific alternatives among siblings (e.g., 'economic__bls-series' for labor data), though the dataset list helps differentiate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.